Emerging Trends in Engineering, Science and Technology for Society, Energy and Environment

Vanchipura, Rajesh; Jiji, K.S.

doi:10.1201/9781351124140

Cited by 3 publications

(1 citation statement)

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“…Researchers have long been interested in the influence of road patterns on behaviour, such as driving performance, traffic safety [17] and route choice. Jacob et al [18] and Green [19] found an ISPRS Int. J. Geo-Inf.…”

Section: Introductionmentioning

confidence: 99%

Differences in the Gaze Behaviours of Pedestrians Navigating between Regular and Irregular Road Patterns

Liu

Dong

Zhan

et al. 2020

IJGI

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While a road pattern influences wayfinding and navigation, its influence on the gaze behaviours of navigating pedestrians is not well documented. In this study, we compared gaze behaviour differences between regular and irregular road patterns using eye-tracking technology. Twenty-one participants performed orientation (ORI) and shortest route selection (SRS) tasks with both road patterns. We used accuracy of answers and response time to estimate overall performance and time to first fixation duration, average fixation duration, fixation count and fixation duration to estimate gaze behaviour. The results showed that participants performed better with better accuracy of answers using irregular road patterns. For both tasks and both road patterns, the Label areas of interest (AOIs) (including shops and signs) received quicker or greater attention. The road patterns influenced gaze behaviour for both Road AOIs and Label AOIs but exhibited a greater influence on Road AOIs in both tasks. In summary, for orientation and route selection, users are more likely to rely on labels, and roads with irregular patterns are important. These findings may serve as the anchor point for determining how people’s gaze behaviours differ depending on road pattern and indicate that labels and unique road patterns should be highlighted for better wayfinding and navigation.

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Section: Introductionmentioning

confidence: 99%

Differences in the Gaze Behaviours of Pedestrians Navigating between Regular and Irregular Road Patterns

Liu

Dong

Zhan

et al. 2020

IJGI

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A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study

Maione,

Lino,

Maione

et al. 2024

Algorithms

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The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components.

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A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

et al. 2023

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According to an extensive evaluation of published studies, there is a shortage of research on systematic literature reviews related to machine learning prediction techniques and methodologies in soil improvement using green materials. A literature review suggests that machine learning algorithms are effective at predicting various soil characteristics, including compressive strength, deformations, bearing capacity, California bearing ratio, compaction performance, stress–strain behavior, geotextile pullout strength behavior, and soil classification. The current study aims to comprehensively evaluate recent breakthroughs in machine learning algorithms for soil improvement using a systematic procedure known as PRISMA and meta-analysis. Relevant databases, including Web of Science, ScienceDirect, IEEE, and SCOPUS, were utilized, and the chosen papers were categorized based on: the approach and method employed, year of publication, authors, journals and conferences, research goals, findings and results, and solution and modeling. The review results will advance the understanding of civil and geotechnical designers and practitioners in integrating data for most geotechnical engineering problems. Additionally, the approaches covered in this research will assist geotechnical practitioners in understanding the strengths and weaknesses of artificial intelligence algorithms compared to other traditional mathematical modeling techniques.

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Emerging Trends in Engineering, Science and Technology for Society, Energy and Environment

Cited by 3 publications

References 0 publications

Differences in the Gaze Behaviours of Pedestrians Navigating between Regular and Irregular Road Patterns

Differences in the Gaze Behaviours of Pedestrians Navigating between Regular and Irregular Road Patterns

A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study

A Systematic Review of Machine Learning Techniques and Applications in Soil Improvement Using Green Materials

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